






import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation

def   _lowerCamelCase						(    __A     :				List[str]			)   ->       Optional[Any]:
    _UpperCAmelCase					:   List[str]      	=					384
    _UpperCAmelCase					:   Optional[Any]      	=					7
    if "tiny" in model_name:
        _UpperCAmelCase					:   int      	=					96
        _UpperCAmelCase					:   Any      	=					(2, 2, 6, 2)
        _UpperCAmelCase					:   List[str]      	=					(3, 6, 12, 24)
    elif "small" in model_name:
        _UpperCAmelCase					:   Any      	=					96
        _UpperCAmelCase					:   List[Any]      	=					(2, 2, 18, 2)
        _UpperCAmelCase					:   int      	=					(3, 6, 12, 24)
    elif "base" in model_name:
        _UpperCAmelCase					:   Optional[int]      	=					128
        _UpperCAmelCase					:   Optional[int]      	=					(2, 2, 18, 2)
        _UpperCAmelCase					:   Optional[Any]      	=					(4, 8, 16, 32)
        _UpperCAmelCase					:   Any      	=					12
        _UpperCAmelCase					:   Optional[int]      	=					512
    elif "large" in model_name:
        _UpperCAmelCase					:   Tuple      	=					192
        _UpperCAmelCase					:   Optional[Any]      	=					(2, 2, 18, 2)
        _UpperCAmelCase					:   Optional[int]      	=					(6, 12, 24, 48)
        _UpperCAmelCase					:   Any      	=					12
        _UpperCAmelCase					:   Union[str, Any]      	=					768

    # set label information
    _UpperCAmelCase					:   Optional[Any]      	=					150
    _UpperCAmelCase					:   int      	=					'''huggingface/label-files'''
    _UpperCAmelCase					:   Any      	=					'''ade20k-id2label.json'''
    _UpperCAmelCase					:   Optional[Any]      	=					json.load(open(hf_hub_download(__A		,		__A		,		repo_type='''dataset'''			)		,		'''r'''			)			)
    _UpperCAmelCase					:   List[str]      	=					{int(__A			): v for k, v in idalabel.items()}
    _UpperCAmelCase					:   Dict      	=					{v: k for k, v in idalabel.items()}

    _UpperCAmelCase					:   Tuple      	=					SwinConfig(
        embed_dim=__A		,		depths=__A		,		num_heads=__A		,		window_size=__A		,		out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']		,		)
    _UpperCAmelCase					:   List[str]      	=					UperNetConfig(
        backbone_config=__A		,		auxiliary_in_channels=__A		,		num_labels=__A		,		idalabel=__A		,		labelaid=__A		,		)

    return config

def   _lowerCamelCase						(    __A     :				Union[str, Any]			)   ->       int:
    _UpperCAmelCase					:   Optional[int]      	=					[]

    # fmt: off
    # stem
    rename_keys.append(('''backbone.patch_embed.projection.weight''', '''backbone.embeddings.patch_embeddings.projection.weight''')			)
    rename_keys.append(('''backbone.patch_embed.projection.bias''', '''backbone.embeddings.patch_embeddings.projection.bias''')			)
    rename_keys.append(('''backbone.patch_embed.norm.weight''', '''backbone.embeddings.norm.weight''')			)
    rename_keys.append(('''backbone.patch_embed.norm.bias''', '''backbone.embeddings.norm.bias''')			)
    # stages
    for i in range(len(config.backbone_config.depths			)			):
        for j in range(config.backbone_config.depths[i]			):
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')			)
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')			)
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')			)
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')			)
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')			)
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')			)
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')			)
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')			)
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')			)
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')			)
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight''')			)
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias''')			)

        if i < 3:
            rename_keys.append((f'''backbone.stages.{i}.downsample.reduction.weight''', f'''backbone.encoder.layers.{i}.downsample.reduction.weight''')			)
            rename_keys.append((f'''backbone.stages.{i}.downsample.norm.weight''', f'''backbone.encoder.layers.{i}.downsample.norm.weight''')			)
            rename_keys.append((f'''backbone.stages.{i}.downsample.norm.bias''', f'''backbone.encoder.layers.{i}.downsample.norm.bias''')			)
        rename_keys.append((f'''backbone.norm{i}.weight''', f'''backbone.hidden_states_norms.stage{i+1}.weight''')			)
        rename_keys.append((f'''backbone.norm{i}.bias''', f'''backbone.hidden_states_norms.stage{i+1}.bias''')			)

    # decode head
    rename_keys.extend(
        [
            ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
            ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
            ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
            ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
        ]			)
    # fmt: on

    return rename_keys

def   _lowerCamelCase						(    __A     :				str		,		__A     :				Any		,		__A     :				Any			)   ->       str:
    _UpperCAmelCase					:   List[Any]      	=					dct.pop(__A			)
    _UpperCAmelCase					:   List[str]      	=					val

def   _lowerCamelCase						(    __A     :				Tuple		,		__A     :				Tuple			)   ->       str:
    _UpperCAmelCase					:   List[Any]      	=					[int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
    for i in range(len(backbone_config.depths			)			):
        _UpperCAmelCase					:   List[str]      	=					num_features[i]
        for j in range(backbone_config.depths[i]			):
            # fmt: off
            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
            _UpperCAmelCase					:   Union[str, Any]      	=					state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''			)
            _UpperCAmelCase					:   Dict      	=					state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias'''			)
            # next, add query, keys and values (in that order) to the state dict
            _UpperCAmelCase					:   List[Any]      	=					in_proj_weight[:dim, :]
            _UpperCAmelCase					:   Optional[Any]      	=					in_proj_bias[: dim]
            _UpperCAmelCase					:   Optional[Any]      	=					in_proj_weight[
                dim : dim * 2, :
            ]
            _UpperCAmelCase					:   Tuple      	=					in_proj_bias[
                dim : dim * 2
            ]
            _UpperCAmelCase					:   List[Any]      	=					in_proj_weight[
                -dim :, :
            ]
            _UpperCAmelCase					:   Optional[Any]      	=					in_proj_bias[-dim :]
            # fmt: on

def   _lowerCamelCase						(    __A     :				Any			)   ->       Optional[int]:
    _UpperCAmelCase	,			_UpperCAmelCase					:   List[Any]      	=					x.shape
    _UpperCAmelCase					:   Optional[Any]      	=					x.reshape(__A		,		4		,		in_channel // 4			)
    _UpperCAmelCase					:   str      	=					x[:, [0, 2, 1, 3], :].transpose(1		,		2			).reshape(__A		,		__A			)
    return x

def   _lowerCamelCase						(    __A     :				Optional[Any]			)   ->       int:
    _UpperCAmelCase	,			_UpperCAmelCase					:   List[str]      	=					x.shape
    _UpperCAmelCase					:   List[Any]      	=					x.reshape(__A		,		in_channel // 4		,		4			)
    _UpperCAmelCase					:   List[Any]      	=					x[:, :, [0, 2, 1, 3]].transpose(1		,		2			).reshape(__A		,		__A			)

    return x

def   _lowerCamelCase						(    __A     :				Tuple			)   ->       List[str]:
    _UpperCAmelCase					:   List[str]      	=					x.shape[0]
    _UpperCAmelCase					:   Tuple      	=					x.reshape(4		,		in_channel // 4			)
    _UpperCAmelCase					:   Any      	=					x[[0, 2, 1, 3], :].transpose(0		,		1			).reshape(__A			)
    return x

def   _lowerCamelCase						(    __A     :				Tuple			)   ->       Union[str, Any]:
    _UpperCAmelCase					:   Optional[Any]      	=					x.shape[0]
    _UpperCAmelCase					:   Dict      	=					x.reshape(in_channel // 4		,		4			)
    _UpperCAmelCase					:   int      	=					x[:, [0, 2, 1, 3]].transpose(0		,		1			).reshape(__A			)
    return x

def   _lowerCamelCase						(    __A     :				List[Any]		,		__A     :				int		,		__A     :				Dict			)   ->       Union[str, Any]:
    _UpperCAmelCase					:   Dict      	=					{
        '''upernet-swin-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth''',
        '''upernet-swin-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth''',
        '''upernet-swin-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth''',
        '''upernet-swin-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth''',
    }
    _UpperCAmelCase					:   int      	=					model_name_to_url[model_name]
    _UpperCAmelCase					:   Tuple      	=					torch.hub.load_state_dict_from_url(__A		,		map_location='''cpu'''		,		file_name=__A			)[
        '''state_dict'''
    ]

    for name, param in state_dict.items():
        print(__A		,		param.shape			)

    _UpperCAmelCase					:   Union[str, Any]      	=					get_upernet_config(__A			)
    _UpperCAmelCase					:   List[str]      	=					UperNetForSemanticSegmentation(__A			)
    model.eval()

    # replace "bn" => "batch_norm"
    for key in state_dict.copy().keys():
        _UpperCAmelCase					:   int      	=					state_dict.pop(__A			)
        if "bn" in key:
            _UpperCAmelCase					:   Optional[Any]      	=					key.replace('''bn'''		,		'''batch_norm'''			)
        _UpperCAmelCase					:   Optional[int]      	=					val

    # rename keys
    _UpperCAmelCase					:   int      	=					create_rename_keys(__A			)
    for src, dest in rename_keys:
        rename_key(__A		,		__A		,		__A			)
    read_in_q_k_v(__A		,		config.backbone_config			)

    # fix downsample parameters
    for key, value in state_dict.items():
        if "downsample" in key:
            if "reduction" in key:
                _UpperCAmelCase					:   str      	=					reverse_correct_unfold_reduction_order(__A			)
            if "norm" in key:
                _UpperCAmelCase					:   Dict      	=					reverse_correct_unfold_norm_order(__A			)

    model.load_state_dict(__A			)

    # verify on image
    _UpperCAmelCase					:   Any      	=					'''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
    _UpperCAmelCase					:   Any      	=					Image.open(requests.get(__A		,		stream=__A			).raw			).convert('''RGB'''			)

    _UpperCAmelCase					:   Dict      	=					SegformerImageProcessor()
    _UpperCAmelCase					:   int      	=					processor(__A		,		return_tensors='''pt'''			).pixel_values

    with torch.no_grad():
        _UpperCAmelCase					:   List[Any]      	=					model(__A			)
        _UpperCAmelCase					:   Dict      	=					outputs.logits

    print(logits.shape			)
    print('''First values of logits:'''		,		logits[0, 0, :3, :3]			)
    # assert values
    if model_name == "upernet-swin-tiny":
        _UpperCAmelCase					:   str      	=					torch.tensor(
            [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]			)
    elif model_name == "upernet-swin-small":
        _UpperCAmelCase					:   Dict      	=					torch.tensor(
            [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]			)
    elif model_name == "upernet-swin-base":
        _UpperCAmelCase					:   Dict      	=					torch.tensor(
            [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]			)
    elif model_name == "upernet-swin-large":
        _UpperCAmelCase					:   Dict      	=					torch.tensor(
            [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]			)
    print('''Logits:'''		,		outputs.logits[0, 0, :3, :3]			)
    assert torch.allclose(outputs.logits[0, 0, :3, :3]		,		__A		,		atol=1e-4			)
    print('''Looks ok!'''			)

    if pytorch_dump_folder_path is not None:
        print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''			)
        model.save_pretrained(__A			)
        print(f'''Saving processor to {pytorch_dump_folder_path}'''			)
        processor.save_pretrained(__A			)

    if push_to_hub:
        print(f'''Pushing model and processor for {model_name} to hub'''			)
        model.push_to_hub(f'''openmmlab/{model_name}'''			)
        processor.push_to_hub(f'''openmmlab/{model_name}'''			)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE							=    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--model_name',
      default='upernet-swin-tiny',
      type=str,
      choices=[F'upernet-swin-{size}' for size in ['tiny', 'small', 'base', 'large']],
      help='Name of the Swin + UperNet model you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
  )
  parser.add_argument(
      '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
  )

  SCREAMING_SNAKE_CASE							=    parser.parse_args()
  convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'							)

		_lowerCamelCase				   =  str(bin(UpperCamelCase							)							)
		binary_number += "0" * shift_amount
		return binary_number


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'							)

		_lowerCamelCase				   =  str(bin(UpperCamelCase							)							)[2:]
		if shift_amount >= len(UpperCamelCase							):
				return "0b0"
		_lowerCamelCase				   =  binary_number[: len(UpperCamelCase							) - shift_amount]
		return "0b" + shifted_binary_number


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number >= 0:  # Get binary representation of positive number
				_lowerCamelCase				   =  '0' + str(bin(UpperCamelCase							)							).strip('-'							)[2:]
		else:  # Get binary (2's complement) representation of negative number
				_lowerCamelCase				   =  len(bin(UpperCamelCase							)[3:]							)  # Find 2's complement of number
				_lowerCamelCase				   =  bin(abs(UpperCamelCase							) - (1 << binary_number_length)							)[3:]
				_lowerCamelCase				   =  (
				    '1' + '0' * (binary_number_length - len(UpperCamelCase							)) + binary_number
				)

		if shift_amount >= len(UpperCamelCase							):
				return "0b" + binary_number[0] * len(UpperCamelCase							)
		return (
		    "0b"
		    + binary_number[0] * shift_amount
		    + binary_number[: len(UpperCamelCase							) - shift_amount]
		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()